Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm
This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective searc...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9837912/ |
| _version_ | 1811223197927866368 |
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| author | Truong Hoang Bao Huy Daehee Kim Dieu Ngoc Vo |
| author_facet | Truong Hoang Bao Huy Daehee Kim Dieu Ngoc Vo |
| author_sort | Truong Hoang Bao Huy |
| collection | DOAJ |
| description | This paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions. |
| first_indexed | 2024-04-12T08:28:45Z |
| format | Article |
| id | doaj.art-7f0ba401fa8c447195be285f6d4d43e1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T08:28:45Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7f0ba401fa8c447195be285f6d4d43e12022-12-22T03:40:17ZengIEEEIEEE Access2169-35362022-01-0110778377785610.1109/ACCESS.2022.31933719837912Multiobjective Optimal Power Flow Using Multiobjective Search Group AlgorithmTruong Hoang Bao Huy0https://orcid.org/0000-0002-7742-8967Daehee Kim1https://orcid.org/0000-0001-9591-0055Dieu Ngoc Vo2https://orcid.org/0000-0001-8653-5724Department of Future Convergence Technology, Soonchunhyang University, Asan, South KoreaDepartment of Future Convergence Technology, Soonchunhyang University, Asan, South KoreaDepartment of Power Systems, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, VietnamThis paper proposes a new multi-objective method that efficiently solves the multi-objective optimal power flow (MOOPF) problem in power systems. The objective of solving the MOOPF problem is to concurrently optimize the fuel cost, emissions, and active power loss. The proposed multi-objective search group algorithm (MOSGA) is an effective method that combines the merits of the original search group algorithm with fast nondominated sorting, crowding distance, and archive selection strategies to acquire a nondominated set in a single run. The MOSGA is employed on IEEE 30-bus and 57-bus systems to validate its robustness and efficiency. It was found that implementing MOSGA to solve the MOOPF significantly enhanced the performance of power systems in terms of economic, environmental, and technical benefits. As for Case 6, the fuel cost, emissions, and active power loss were reduced by 16.5707%, 52.0605%, and 60.9443%, respectively. The simulation results were analyzed and compared with those of previously reported studies based on the best individual solutions, compromise solutions, and performance indicators. The comparative results confirmed the potential and advantage of MOSGA when solving the MOOPF problem efficiently and MOSGA had high-quality optimal solutions.https://ieeexplore.ieee.org/document/9837912/Multi-objective search group algorithmmulti-objective optimal power flowfuel costemissions |
| spellingShingle | Truong Hoang Bao Huy Daehee Kim Dieu Ngoc Vo Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm IEEE Access Multi-objective search group algorithm multi-objective optimal power flow fuel cost emissions |
| title | Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm |
| title_full | Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm |
| title_fullStr | Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm |
| title_full_unstemmed | Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm |
| title_short | Multiobjective Optimal Power Flow Using Multiobjective Search Group Algorithm |
| title_sort | multiobjective optimal power flow using multiobjective search group algorithm |
| topic | Multi-objective search group algorithm multi-objective optimal power flow fuel cost emissions |
| url | https://ieeexplore.ieee.org/document/9837912/ |
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